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Traffic Flow Prediction with Rainfall Impact Using a Deep Learning Method
Joint Authors
Jia, Yuhan
Wu, Jianping
Xu, Ming
Source
Journal of Advanced Transportation
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-08-09
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
Accurate traffic flow prediction is increasingly essential for successful traffic modeling, operation, and management.
Traditional data driven traffic flow prediction approaches have largely assumed restrictive (shallow) model architectures and do not leverage the large amount of environmental data available.
Inspired by deep learning methods with more complex model architectures and effective data mining capabilities, this paper introduces the deep belief network (DBN) and long short-term memory (LSTM) to predict urban traffic flow considering the impact of rainfall.
The rainfall-integrated DBN and LSTM can learn the features of traffic flow under various rainfall scenarios.
Experimental results indicate that, with the consideration of additional rainfall factor, the deep learning predictors have better accuracy than existing predictors and also yield improvements over the original deep learning models without rainfall input.
Furthermore, the LSTM can outperform the DBN to capture the time series characteristics of traffic flow data.
American Psychological Association (APA)
Jia, Yuhan& Wu, Jianping& Xu, Ming. 2017. Traffic Flow Prediction with Rainfall Impact Using a Deep Learning Method. Journal of Advanced Transportation،Vol. 2017, no. 2017, pp.1-10.
https://search.emarefa.net/detail/BIM-1170905
Modern Language Association (MLA)
Jia, Yuhan…[et al.]. Traffic Flow Prediction with Rainfall Impact Using a Deep Learning Method. Journal of Advanced Transportation No. 2017 (2017), pp.1-10.
https://search.emarefa.net/detail/BIM-1170905
American Medical Association (AMA)
Jia, Yuhan& Wu, Jianping& Xu, Ming. Traffic Flow Prediction with Rainfall Impact Using a Deep Learning Method. Journal of Advanced Transportation. 2017. Vol. 2017, no. 2017, pp.1-10.
https://search.emarefa.net/detail/BIM-1170905
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1170905